Abstract

Objectives:
To develop methods for performing
expected value of perfect information (EVPI) analysis in
computationally expensive models and to report on the
developments on the health economics of interferon-β
and glatiramer acetate in the management of multiple
sclerosis (MS) using this methodological framework.

Review methods:
A methodological framework was
developed for undertaking EVPI analysis for complex
models. The framework identifies conditions whereby
EVPI may be calculated numerically, where the onelevel
algorithm sufficiently approximates the two-level
algorithm, and whereby metamodelling techniques may
accurately approximate the original simulation model.
Metamodelling techniques, including linear regression,
neural networks and Gaussian processes (GP), were
systematically reviewed and critically appraised. Linear
regression metamodelling, GP metamodelling and the
one-level EVPI approximation were used to estimate
partial EVPIs using the ScHARR MS cost-effectiveness
model.

Results:
The review of metamodelling approaches
suggested that in general the simpler techniques such as
linear regression may be easier to implement, as they
require little specialist expertise although may provide
only limited predictive accuracy. More complex
methods such as Gaussian process metamodelling and
neural networks tend to use less-restrictive assumptions
concerning the relationship between the model inputs
and net benefits, and therefore may permit greater
accuracy in estimating EVPIs. Assuming independent
treatment efficacy, the ‘per patient’ EVPI for all
uncertainty parameters within the ScHARR MS model is
£8855. This leads to a population EVPI of £86,208,936,
which represents the upper estimate for the overall
EVPI over 10 years. Assuming all treatment efficacies
are perfectly correlated, the overall per patient EVPI is
£4271. This leads to a population EVPI of £41,581,273,
which represents the lower estimate for the overall
EVPI over 10 years. The partial EVPI analysis,
undertaken using both the linear regression metamodel
and Gaussian process metamodel clearly, suggests
that further research is indicated on the long-term
impact of these therapies on disease progression, the
proportion of patients dropping off therapy and the
relationship between the EDSS, quality of life and costs
of care.

Conclusions:
The applied methodology points towards
using more sophisticated metamodelling approaches in
order to obtain greater accuracy in EVPI estimation.
Programming requirements, software availability and
statistical accuracy should be considered when
choosing between metamodelling techniques. Simpler,
more accessible techniques are open to greater
predictive error, whilst sophisticated methodologies
may enhance accuracy within non-linear models, but
are considerably more difficult to implement and may
require specialist expertise. These techniques have
been applied in only a limited number of cases hence
their suitability for use in EVPI analysis has not yet been
demonstrated. A number of areas requiring further
research have been highlighted. Further clinical
research is required concerning the relationship
between the EDSS, costs of care and health outcomes,
the rates at which patients drop off therapy and in
particular the impact of disease-modifying therapies on
the progression of MS. Further methodological
research is indicated concerning the inclusion of
epidemiological population parameters within the
sensitivity analysis; the development of criteria for
selecting a metamodelling approach; the application of
metamodelling techniques within health economic
models and in the specific application to EVI analyses;
and the use of metamodelling for EVSI and ENBS
analysis.

Item Type:

Monograph
(Technical Report)

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